Are workers’ comp violations more common where income disparity is high?

Data Editor

A pair of researchers sought to find out whether certain labor violations were concentrated in areas with more intense economic inequality. They shared their work with Trend CT.

Daniel Ravizza, a researcher for the New England Regional Council of Carpenters, with the assistance of Matthew Zagaja, an attorney and fellow at Harvard’s Berkman Center for Internet andSociety, compared the frequency of stop-work orders with a measure of economic inequality in the map above.

What are stop work orders?

When employers fail to provide proper workers’ compensation insurance, the State Department of Labor can order them to stop work.

Stop-work orders are used to penalize only worker’s compensation violations. Other labor laws, such as those relating to working conditions, are enforced with other types of penalties, said Gary Pechie, director of the Department of Labor Wage and Workplace Standards Division.

Failure to provide proper workers’ compensation coverage can stem from other violations. For instance, Pechie said, an employee might not be listed on the payroll at all, or a construction worker with a hazardous job might be listed on the books as an indoor office worker who doesn’t face the same risks, Pechie said.

Who pays?

When proper workers’ compensation isn’t provided, the state’s Second Injury Fund, paid for by an assessment on employers, can be on the hook, at least temporarily. “During fiscal year 2015, the Second Injury Fund was ordered to pay $3.9 million to injured employees where the employer wase uninsured and failed to pay benefits,” State Treasurer Denise L. Nappier wrote in an email to Trend Monday. “As of June 30, 2015, the Second Injury Fund had 703 open cases involving uninsured employers.”

But the offending employers aren’t off the hook if the state steps in.

“Once the fund pays benefits, the employer is liable to the fund for these payments,” Nappier said. “The fund is permitted to seek to recover the money expended (i.e.: lawsuits or liens on bank accounts, real or personal property, etc.). During fiscal year 2015, the fund recovered $775,000 through the collection process from uninsured employers.”

Mapping the violations

In June Ravizza and Zagaja retrieved a list of more than 1,200 stop-work orders issued by the Department of Labor since 2004. The data, periodically updated and therefore subject to change, is available on the Department of Labor site (PDF).

The researchers created the above map with two layers. The color of each location (based on census tract shapes), illustrates the Gini coefficient, a measure of income inequality used by the U.S. Census Bureau. On top of that is an animated heat layer showing the concentration of stop-work orders.

“After reading Dr. David Weil’s report to the US Dept. of Labor on the ‘fissuring’ of the employee/employer relationship, I wanted to dig deeper into local examples of worker misclassification and worker’s compensation insurance gaps,” Ravizza said. “These practices on the part of employers ultimately shift risk downward to those who can least afford it. As Connecticut has large gaps between the ultra-wealthy and the poor and working class, I wanted to see what the state’s stop-work order list could tell us about where the stop-work orders were happening and who they were happening to.”

Is economic inequality a factor?

There are individual locations with a high Gini coefficient and a large number of stop-work orders.

For instance, Stamford has a relatively high Gini coefficient of 0.5082 and 184 stop work orders issued, the most in the state for the period analyzed. New Haven, with a higher Gini coefficient of 0.5122, had 50 stop work orders, putting it at seventh on the list of towns with the most stop work orders.

However, Trend CT found the correlation between the coefficient and the number of stop work orders to be weak at 0.38. In other words, economic inequality in a certain location wasn’t a good predictor of where violations would occur.

There were stronger correlations between population and stop-work orders, at 0.69, and with sales tax revenue, at 0.6.

“While there is a much smaller correlation than I originally thought, this heatmap has allowed us to see which communities have had high instances of stop-work orders,” Ravizza said. “My hope is that it can make the issue easier to visualize and will allow reporting and enforcement to become more efficient.”

Ravizza, coming from a labor standpoint, is concerned not just about worker’s compensation, but about illegal payroll practices which can be at the root of an employer’s failure to provide workers’ compensation insurance.

“Not paying payroll taxes and skipping out on required insurance harms hard-working people and erodes our tax base. It’s a toxic trend within our industry, and our union is actively working to educate both the public and our membership on how to spot and stop worker misclassification and wage theft. Beyond the construction world, it’s pervasive in many, many parts of our economy and my hope is that increased visibility of the issue and enforcement can bring justice to workers and taxpayers alike.”

Fines work, usually

The stop-work orders, and a $1,000-per-day fine for continuing work after a stop-work order has been issued, are strong deterrents in many cases, Pechie said.

“We’ll stop work on Tuesday, and Wednesday morning at 8 o’clock people are here with checks,” he said.

However, some companies work through the stop-work orders, despite the $1,000-a-day fine, perhaps determining the cost of delay would be even higher.

“Some companies, it’s worth [it to] them to keep working,” Pechie said, citing a case of construction at the Westfarms mall Apple Store, where Carlsbad, Calif.-based Dickinson Cameron Construction was issued a stop-work order in June 2014. “They worked right through our stop-work order and paid us $3,000.” The order was resolved after three days.

That’s why his department has sought to raise the fine to $2,500 per day for firms that ignore stop-work orders. “We wanted to make it more of a deterrent,” Pechie said. A bill was raised this year, but it was referred by the Senate to the Committee on Finance, Revenue and Bonding in May and wasn’t passed.

How effective?

Trend CT’s analyzed the same data set as Ravizza and Zagaja (although updated since the two retrieved it in the summer) and found that the average duration between the issuance of a stop-work order and its release was almost 16 days. However, the average can be skewed by orders that are not resolved for a very long time. The mode — the most frequent duration — was one day. The median was six days.

The following chart groups released cases by theduration between issuance of a stop-work order and its release.

More about the state’s fund

Trend CT asked Nappier about the Second Injury Fund in the context of quantifying the cost to taxpayers of these worker’s comp-related violations. She pointed out that the fund is fully paid for by assessments on employers, not through other taxes, such as residents’ income taxes.

In addition, she points to an overall reduction of these assessments on employers over the past 16 years. She said that assessments have gone down or stayed the same since 1999.

“During my administration, stricter oversight allowed a reduction of rates charged to Connecticut businesses for assessments paid to the Treasury’s Second Injury Fund. The cumulative effect of all the rate reductions since 1999 is a net savings of $1.1 billion for Connecticut businesses, which includes $110 million in projected savings during Fiscal Year 2016.”

Jake is a former managing editor of The Ridgefield Press, a Hersam Acorn newspaper. He worked for the community newspaper chain as a reporter and editor for five years before joining the Mirror staff. He studied professional writing at Western Connecticut State University and is a graduate student in software engineering at Harvard Extension School.

What do you think?

Joseph Brzezinski

Actually a correlation of .39 should be considered substantial. If the correlations with population and sales tax are based upon raw counts, those higher correlation largely reflect simply scaling. A procedure to get some measure of intercorrelations would be usefull as well, say a simple linear regression.

Jake Kara

Thanks, Joseph. Here’s a spreadsheet with the data if you want to analyze it further. We’d be glad to see what you come up with.

I ran linear regressions both with the raw data and regressing the ratio of stop orders to population (expressed as odds) against Gini, absolute margin of error, and sales tax per population for towns with stop orders > 0. On raw basis, only pop and sales tax are significant. R-Square .56.
With the odds for towns with 1 or more stop orders, pretty much the same R-Square .55.
The margin of error for the Gini complicates these calculations and may be a cause for the lack of significance. May some other measure of income disparity would be more useful — possibly, median income or some proxy for Gini. I often use the ratio of total income in top 20% to total income in the bottom 20% as a proxy.

Jake Kara

Thanks for analyzing this further, Joe. There may well be more significant measures than what we looked at here.